sfepy.solvers.optimize module¶
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class
sfepy.solvers.optimize.
FMinSteepestDescent
(conf, **kwargs)[source]¶ Steepest descent optimization solver.
Kind: ‘opt.fmin_sd’
For common configuration parameters, see
Solver
.Specific configuration parameters:
Parameters: i_max : int (default: 10)
The maximum number of iterations.
eps_rd : float (default: 1e-05)
The relative delta of the objective function.
eps_of : float (default: 0.0001)
The tolerance for the objective function.
eps_ofg : float (default: 1e-08)
The tolerance for the objective function gradient.
norm : numpy norm (default: inf)
The norm to be used.
ls : bool (default: True)
If True, use a line-search.
ls_method : {‘backtracking’, ‘full’} (default: ‘backtracking’)
The line-search method.
ls_on : float (default: 0.99999)
Start the backtracking line-search by reducing the step, if ||f(x^i)|| / ||f(x^{i-1})|| is larger than ls_on.
ls0 : 0.0 < float < 1.0 (default: 1.0)
The initial step.
ls_red : 0.0 < float < 1.0 (default: 0.5)
The step reduction factor in case of correct residual assembling.
ls_red_warp : 0.0 < float < 1.0 (default: 0.1)
The step reduction factor in case of failed residual assembling (e.g. the “warp violation” error caused by a negative volume element resulting from too large deformations).
ls_min : 0.0 < float < 1.0 (default: 1e-05)
The minimum step reduction factor.
check : 0, 1 or 2 (default: 0)
If >= 1, check the tangent matrix using finite differences. If 2, plot the resulting sparsity patterns.
delta : float (default: 1e-06)
If check >= 1, the finite difference matrix is taken as A_{ij} = \frac{f_i(x_j + \delta) - f_i(x_j - \delta)}{2 \delta}.
output : function
If given, use it instead of
output()
function.yscales : list of str (default: [‘linear’, ‘log’, ‘log’, ‘linear’])
The list of four convergence log subplot scales.
log : dict or None
If not None, log the convergence according to the configuration in the following form:
{'text' : 'log.txt', 'plot' : 'log.pdf'}
. Each of the dict items can be None.-
name
= 'opt.fmin_sd'¶
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class
sfepy.solvers.optimize.
ScipyFMinSolver
(conf, **kwargs)[source]¶ Interface to SciPy optimization solvers scipy.optimize.fmin_*.
Kind: ‘nls.scipy_fmin_like’
For common configuration parameters, see
Solver
.Specific configuration parameters:
Parameters: method : {‘fmin’, ‘fmin_bfgs’, ‘fmin_cg’, ‘fmin_cobyla’, ‘fmin_l_bfgs_b’, ‘fmin_ncg’, ‘fmin_powell’, ‘fmin_slsqp’, ‘fmin_tnc’} (default: ‘fmin’)
The actual optimization method to use.
i_max : int (default: 10)
The maximum number of iterations.
* : *
Additional parameters supported by the method.
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name
= 'nls.scipy_fmin_like'¶
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